A hybrid soft sensor framework for real-time biodiesel yield prediction: Integrating mechanistic models and machine learning algorithms
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DOI: 10.1016/j.renene.2024.121888
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- Wancura, João H.C. & Brondani, Michel & dos Santos, Maicon S.N. & Oro, Carolina E.D. & Wancura, Guilherme C. & Tres, Marcus V. & Oliveira, J. Vladimir, 2023. "Demystifying the enzymatic biodiesel: How lipases are contributing to its technological advances," Renewable Energy, Elsevier, vol. 216(C).
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- Suvarna, Manu & Jahirul, Mohammad Islam & Aaron-Yeap, Wai Hung & Augustine, Cheryl Valencia & Umesh, Anushri & Rasul, Mohammad Golam & Günay, Mehmet Erdem & Yildirim, Ramazan & Janaun, Jidon, 2022. "Predicting biodiesel properties and its optimal fatty acid profile via explainable machine learning," Renewable Energy, Elsevier, vol. 189(C), pages 245-258.
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- Shelare, Sagar D. & Belkhode, Pramod N. & Nikam, Keval Chandrakant & Jathar, Laxmikant D. & Shahapurkar, Kiran & Soudagar, Manzoore Elahi M. & Veza, Ibham & Khan, T.M. Yunus & Kalam, M.A. & Nizami, Ab, 2023. "Biofuels for a sustainable future: Examining the role of nano-additives, economics, policy, internet of things, artificial intelligence and machine learning technology in biodiesel production," Energy, Elsevier, vol. 282(C).
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Keywords
Artificial intelligence; Artificial neural network; Biodiesel; Machine learning; Soft sensor;All these keywords.
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